In many cases, scenes of interest in sequences of images recorded by an imaging device are partially obscured by particles which interpose themselves between the scenes and the camera. The type of imaging devices may be film, television, infra-red, ultra-violet, X-ray, radar or ultra-sound imagers. Typical examples would be images obtained when looking through falling snow or, in the underwater domain, looking through organic particles or other particles stirred up by natural or artificial water currents. The presence of such particles imposes a severe strain on an operator trying to identify or monitor features of the scene as any one who has driven in a snowstorm during the day, or at night with the headlights on, can testify.
Some cameras adjust themselves to the average light level and will, as a result, operate at a gain or sensitivity more appropriate for looking at the particles than at the scene of interest when the particles are numerous enough and when their luminous intensity is substantially different from the scene being observed.
Another problem occurs when an attempt is made to numerically process these type of images since the presence of particles which partially obscure an image severely affects the performance of standard image-processing methods such as those based on the Fourier transform. The presence of those particles introduce high spatial frequency components different from those present in the scene of interest and those components cannot be adequately treated by even the most efficient algorithms.
In a first known method of processing an image, each image is treated separately and the high spatial frequency components associated with the particles are removed by standard filtering techniques such as direct convolution or Fourier transform operations. In a second method, a sequence of images is averaged or filtered using statistical operators until the effect of the particle is sufficiently attenuated. These two methods can be considered to be in the general category of linear processing methods. However, both of these known methods have a number of drawbacks.
In the case of the convolution methods, there are three principal limitations. The most important limitation is that these methods reduce the high frequency components uniformly and therefore the image of the scene is blurred by a corresponding amount. A second limitation is that these methods do not allow the scene, which lies behind the particles, to be observed. These methods also require substantial computational resources in their digital implementation which is a third limitation.
The principal limitation in the case of the averaging method is that of the averaging process itself since the effect of the particles on the image is not eliminated but merely progressively reduced as more images are added together. The process requires many images to be used resulting in loss of time resolution due to the long sequence of images. Furthermore, a definite loss of contrast occurs due to the averaging of light from all the particles in each image uniformly across the screen.